Sparse estimation for structural variability
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Berger_Sparse estimation.pdf
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748.67 KB
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Checksum (MD5)
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Author(s)
Hosur, Raghavendra
Singh, Rohit
Berger, Bonnie
Date Issued
April 2011
Journal
Algorithms for Molecular Biology
Publisher
BioMed Central Ltd.
Citation
Hosur, Raghavendra, Rohit Singh, and Bonnie Berger. “Sparse Estimation for Structural Variability.” Algorithms for Molecular Biology 6.1 (2011): 12. Web.
Version
Final published version
Abstract
Background
Proteins are dynamic molecules that exhibit a wide range of motions; often these conformational changes are important for protein function. Determining biologically relevant conformational changes, or true variability, efficiently is challenging due to the noise present in structure data.
Results
In this paper we present a novel approach to elucidate conformational variability in structures solved using X-ray crystallography. We first infer an ensemble to represent the experimental data and then formulate the identification of truly variable members of the ensemble (as opposed to those that vary only due to noise) as a sparse estimation problem. Our results indicate that the algorithm is able to accurately distinguish genuine conformational changes from variability due to noise. We validate our predictions for structures in the Protein Data Bank by comparing with NMR experiments, as well as on synthetic data. In addition to improved performance over existing methods, the algorithm is robust to the levels of noise present in real data. In the case of Human Ubiquitin-conjugating enzyme Ubc9, variability identified by the algorithm corresponds to functionally important residues implicated by mutagenesis experiments. Our algorithm is also general enough to be integrated into state-of-the-art software tools for structure-inference.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Mathematics
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/1748-7188-6-12
